Recently a so-called Guided Image Filter has been developed as an edge-preserving smoothing filter. See He et al.: “Guided Image Filtering”, Proceedings of the 11th European conference on Computer vision (ECCV '10) (2010), pp. 1-14. Besides the input for the data to be filtered, the Guided Image Filter provides another input for a guidance image that drives the filtering process. It has been proven to be efficient in a wide range of applications, including alpha matting (see He et al.: “A Global Sampling Method for Alpha Matting”, Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR '11) (2011), pp. 2049-2056), and others. The Guided Image Filter has several key properties and advantages. A first advantage is the preservation of fine details in the filter input. A further advantage is that the filtered output has a higher quality than comparable filters. For example, it does not suffer from gradient reversal artifacts like the Bilateral Filter (see Tomasi et al.: “Bilateral Filtering for gray and color images”, Proceedings of the 1998 IEEE International Conference on Computer Vision (ICCV) (1998), pp. 839-846). Finally, the Guide Image Filter supports an extremely efficient implementation. Its complexity is O(n), where n denotes the number of pixels in the image. Notably, this means that the computational complexity is independent of the chosen size of the filter kernel. No other edge-preserving smoothing filter has this property.
A confidence-aware Bilateral Filter is disclosed in Jachalsky et al.: “Confidence evaluation for robust, fast-converging disparity map refinement”, 2010 IEEE International Conference on Multimedia and Expo (ICME) (2010), pp. 1399-1404. Besides potential gradient reversal artifacts, it suffers from computational complexity, especially for larger kernel sizes.